Papers: User needs and preferences elicitation

Large-Scale Needfinding: Methods of Increasing User-Generated Needs From Large Populations

[+] Author and Article Information
Cory R. Schaffhausen

Department of Mechanical Engineering,
University of Minnesota,
Minneapolis, MN 55455
e-mail: schaf390@umn.edu

Timothy M. Kowalewski

Department of Mechanical Engineering,
University of Minnesota,
Minneapolis, MN 55455
e-mail: timk@umn.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 11, 2014; final manuscript received February 13, 2015; published online May 19, 2015. Assoc. Editor: Carolyn Seepersad.

J. Mech. Des 137(7), 071403 (Jul 01, 2015) (11 pages) Paper No: MD-14-1561; doi: 10.1115/1.4030161 History: Received September 11, 2014; Revised February 13, 2015; Online May 19, 2015

Understanding user needs and preferences is increasingly recognized as a critical component of early stage product development. The large-scale needfinding methods in this series of studies attempt to overcome shortcomings with existing methods, particularly in environments with limited user access. The three studies evaluated three specific types of stimuli to help users describe higher quantities of needs. Users were trained on need statements and then asked to enter as many need statements and optional background stories as possible. One or more stimulus types were presented, including prompts (a type of thought exercise), shared needs, and shared context images. Topics used were general household areas including cooking, cleaning, and trip planning. The results show that users can articulate a large number of needs unaided, and users consistently increased need quantity after viewing a stimulus. A final study collected 1735 needs statements and 1246 stories from 402 individuals in 24 hr. Shared needs and images significantly increased need quantity over other types. User experience (and not expertise) was a significant factor for increasing quantity, but may not warrant exclusive use of high-experience users in practice.

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Fig. 1

Summary schematic of study 1 and study 2

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Fig. 2

Study 3 user interface for entering needs

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Fig. 3

Summary outline of prompt matrix

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Fig. 4

Study 2 comparison of stimulus types

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Fig. 5

Study 3 needs submitted for each help type

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Fig. 6

Study 3 diminishing returns with increasing help (lines at 90%, 95%, and 98% are shown)

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Fig. 7

Needs submitted by each expertise group (group sizes, n, are shown)

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Fig. 8

Needs submitted by each experience group (group sizes, n, are shown)

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Fig. 9

Distribution of needs submitted per person across expertise groups

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Fig. 10

Rates of need entries for studies 2 and 3 (line at 90% shown)




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